Cognitive and heuristics-based emergent financial management

ABSTRACT

Cognitive and heuristics-based emergent financial management is provided. A method includes obtaining data related to an individual, an organization, a process, or combinations thereof. The data is obtained from internal sources, external sources, or combinations thereof. The method also includes creating data sets from the data based on determined classifications of the data. Further, the method includes establishing relationships between the data sets and determining a conclusion based on the relationships. The conclusion is based on a hypothesis that has undergone a test process.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.17/147,501 filed Jan. 13, 2021, and entitled “COGNITIVE ANDHEURISTICS-BASED EMERGENT FINANCIAL MANAGEMENT,” which is a continuationof U.S. patent application Serial No. 15/787,209 filed Oct. 18, 2017,issued as U.S. Pat. No. 10,896,396, entitled “COGNITIVE ANDHEURISTICS-BASED EMERGENT FINANCIAL MANAGEMENT,” which claims thebenefit of U.S. Provisional Patent Application Ser. No. 62/409,643 filedOct. 18, 2016, entitled “COGNITIVE AND HEURISTICS- BASED EMERGENTFINANCIAL MANAGEMENT.” The entirety of the above-noted applications isincorporated by reference herein.

BACKGROUND

The term “big data” has been used to describe sets of data that are ofsuch a very large size and/or complexity that traditional dataprocessing applications are not capable of adequately handing the setsof data. Further, the term also refers to the use of data for varioustypes of analytics, including user behavior analytics, predictiveanalytics, or other advanced forms of data analytics that are designedto extract value from the data. Since data is captured by many devicesand in many forms (e.g., computing devices, cameras, microphones,radio-frequency identification readers, and other forms of computingdevices and/or data capturing devices), there is an ever growing amountof data that is being gathered by a multitude of sources (e.g.,individuals, businesses, corporations, governments, and so on).

SUMMARY

The following presents a simplified summary of the innovation in orderto provide a basic understanding of some aspects of the innovation. Thissummary is not an extensive overview of the innovation. It is notintended to identify key/critical elements of the innovation or todelineate the scope of the innovation. Its sole purpose is to presentsome concepts of the innovation in a simplified form as a prelude to themore detailed description that is presented later.

The various aspects provided herein are related to cognitive andheuristics-based emergent financial management. An aspect relates to asystem that includes a processor and a memory that stores executableinstructions that, when executed by the processor, facilitateperformance of operations. The operations include obtaining data relatedto an individual, an organization, a process, or combinations thereof.The operations also include creating data sets from the data based ondetermined classifications of the data. Further, the operations includeestablishing relationships between the data sets and determining aconclusion based on the relationships. The conclusion may be based on ahypothesis that has undergone a test process.

In an example, the data may be in a structured format, a semi-structuredformat, an unstructured format, or combinations thereof. In anotherexample, the data may be acquired from an internal source. In anadditional or alternative example, the data may be acquired from anexternal source.

Another aspect relates to a method that includes obtaining, by a systemcomprising a processor, data from internal sources, external sources, orcombinations thereof. The method also includes creating, by the system,data sets from the data based on determined classifications of the data.Further, the method includes establishing, by the system, relationshipsbetween the data sets. The method also includes determining, by thesystem, a recommendation based on the relationships.

In an example, the determining comprises performing a test process on ahypothesis. In an alternative or additional example, the determining isbased in part on a feedback loop that represents previousrecommendations (e.g., was the recommendation accurate, inaccurate, andso forth).

A further aspect relates to a computer-readable storage device thatstores executable instructions that, in response to execution, cause asystem comprising a processor to perform operations. The operationsinclude obtaining data related to an individual, an organization, aprocess, or combinations thereof. The operations also include creatingdata sets from the data based on determined classifications of the data.Further, the operations include establishing relationships between thedata sets and determining a conclusion based on the relationships. Theconclusion may be based on a hypothesis that has undergone a testprocess.

To the accomplishment of the foregoing and related ends, certainillustrative aspects of the innovation are described herein inconnection with the following description and the annexed drawings.These aspects are indicative, however, of but a few of the various waysin which the principles of the innovation may be employed and thesubject innovation is intended to include all such aspects and theirequivalents. Other advantages and novel features of the innovation willbecome apparent from the following detailed description of theinnovation when considered in conjunction with the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

Various non-limiting embodiments are further described with reference tothe accompanying drawings in which:

FIG. 1 illustrates an example, non-limiting system configured to providecognitive and heuristics-based emergent financial management, accordingto an aspect;

FIG. 2 illustrates an example, non-limiting generic schematicrepresentation of a capacity architecture, according to an aspect;

FIG. 3 illustrates an example schematic representation of a capacityarchitecture, according to an aspect;

FIG. 4A illustrates a left hand side of an example, non-limitingCapability Architecture Knowledge Base for an Enterprise data map;

FIG. 4B illustrates a right hand side of the Capability ArchitectureKnowledge Base for an Enterprise data map;

FIG. 5 illustrates an example, non-limiting system that providescognitive and heuristics-based emergent financial management, accordingto an aspect;

FIG. 6 illustrates an example, non-limiting system that employsautomated learning to facilitate one or more of the disclosed aspects;

FIG. 7 illustrates an example, non-limiting method for cognitive andheuristics-based emergent financial management, according to an aspect;

FIG. 8 illustrates an example, non-limiting computer-readable medium orcomputer-readable device including processor-executable instructionsconfigured to embody one or more of the aspects set forth herein;

FIG. 9 illustrates an example, non-limiting computing environment whereone or more of the aspects set forth herein are implemented, accordingto one or more aspects.

Appendix A is a document describing various embodiments and featuresassociated with particular aspects of the various aspects—this appendixis to be considered part of the specification; and

Appendix B is a document describing various aspects and featuresassociated with particular embodiments—this appendix is to be consideredpart of the specification.

DETAILED DESCRIPTION

The innovation is now described with reference to the drawings. In thefollowing description, for purposes of explanation, numerous specificdetails are set forth in order to provide a thorough understanding ofthe subject innovation. It may be evident, however, that the innovationmay be practiced without these specific details. In other instances,well-known structures and devices are shown in block diagram form inorder to facilitate describing the innovation.

Various aspects described herein relate to cognitive andheuristics-based emergent financial management. The various aspects areconfigured to provide a classification scheme or taxonomy that organizesthe terabytes of data (e.g., big data), regardless of the form of thedata (e.g., semi-structure, structured, unstructured, and so on). Thevarious aspects may be configured to organize the data by identifyingthe components or the buckets into which the data is classified.Further, the various aspects may be configured to establishrelationships between the data or sets of data in order to create usefulassociations. Based on the organization, identification, andrelationships, conclusions and hypothesis may be develop, which may betested or experimented with in order to prove a certain hypothesis.

It is noted that although the various aspects are discussed herein withrespect to financial management, the disclosed aspects are not limitedto this implementation. Instead, the aspects may be applied to othertechnologies (e.g., cognitive healthcare, insurance, and so on) andfinancial management is utilized as a common theme to assist indescribing the various aspects disclosed herein.

As one example, a customer of a financial entity, such as a bank, maywant to use the bank to not only deposit and save money, but also forthe bank to help her create wealth. However, for a simple question suchas “Should I buy or rent a house?” it may take a week or more for thecustomer to set up an appointment that is convenient for both thecustomer and a financial advisor at the bank. For a more holisticquestion related to building wealth, the customer may be willing to workwith a financial advisor. However, the customer may find the amount ofinformation that needs to be collected is cumbersome to gather. Further,the financial advisor may find that reviewing such a large amount ofdata is not practicable and the data that is able to be processed mightnot be adequate to fully answer the question.

For example, in terms of the future, it might not be known which are thecorrect channels (e.g., sources of data), or which channels arecurrently available, to answer the question. Further, the quality of theinformation available may also be a concern. For example, if thecustomer's question is related to whether she should buy or rent ahouse, she might not be looking for just basic answers. Instead, she maywant the bank to know everything and anything related to who she is as acustomer and as an individual. The information available may includewhat she does on a daily basis, where she is in terms of location, whatare the other things she needs to be aware of in terms of macroeconomicfactors, what are the interest rates, what are her peers doing in termsof buying or renting in the same neighborhood that is of interest, andso on.

Based on all the information collected about the customer over time, apersonal management strategy may be developed for the customer,according to various aspects. Thus, information from various sources iscollected, cleaned up as necessary, organized, and analyzed so thatinsightful suggestions may be provided. The bank is able to tell thecustomer that, based on what is known about the customer going back xnumber of years, and based on where the financial market is headed (aswell as the bank), various suggestions may be provided. Continuing theabove example, information related to what is known about the customerfor the last x number of years, what her peers are doing, where the bankis headed, and other macroeconomic factors may be analyzed. Based onthis analysis, the bank may suggest that the customer wait at leastforty days for the market to settle, then it will be a good time torevisit the decision on whether to buy or rent.

FIG. 1 illustrates an example, non-limiting system 100 configured toprovide cognitive and heuristics-based emergent financial management,according to an aspect. The system 100 includes a data obtainmentmanager 102 that may be configured to obtain data related to anindividual and/or a set of individuals. For example, the individual maybe a customer of a bank and a set of individuals may be family members(e.g., the customer, the customer's spouse, and the customer's child).In an alternative or additional example, the set of individuals may beindividuals that are associated with each other, but are not familymembers (e.g., friends, associated, co-workers, related credit cardholders (e.g., employed at the same company), and so on). In stillanother alternative or additional example, the set of individuals may beof a similar age, geographic location, or other demographic information.

The data obtainment manager 102 may also be configured to obtain datarelated to an organization and/or a process. The organization may be anytype of organization and the processes may be processes within theorganization. Further, the data may be related to previousrecommendations and whether the recommendations were accurate (e.g.,through a feedback loop).

According to some implementations, the data obtainment manager 102 maybe configured to gather the information from internal sources and/orexternal sources. Internal sources may be sources within anorganization, such as a financial entity for example. External sourcesmay be sources that are related to a user, such as a user device, aGlobal Positioning System (GPS) device, or other devices related to theuser, other users or peers, and/or other entities (e.g., retail store,and so on).

As used herein an “entity” or “financial entity” refers to a financialinstitution, such as a bank, persons operating on behalf of thefinancial institution, and/or communication devices managed by thefinancial institution and/or the persons operating on behalf of thefinancial institution. Additionally or alternatively, the entity may bea third party monitoring source or another type of entity that has atrusted relationship with the financial institution.

Also included in the system 100 may be a classification manager 104 thatmay be configured to create data sets from the data acquired by theobtainment manager 102. The data may be in any format includingstructured data, semi-structured data, unstructured data, and so on. Tocreate data sets, the classification manager 104 may be configured toidentify categorizations and/or buckets into which the particular databeing reviewed should be classified.

A relationship manager 106 may be configured to establish relationshipsbetween two or more sets of data. The relationships identify usefulassociations between data contained in respective data sets. Forexample, a first set of data may be related to a second set of data; athird set of data may be related to a fourth set of data; and a fifthset of data may not be related to any other current data sets (e.g., isa standalone set of data).

The system 100 may also include an application manager 108 that may beconfigured to determine one or more conclusions based on therelationships. The one or more conclusions may be unique for aparticular customer or user, or may be generic for a set of users. Theapplication manager 108 may be configured to develop the one or moreconclusions based on one or more hypotheses that have undergoneexperimentation or other testing (e.g., a test process).

Further, the system 100 may include at least one memory 110 that maystore computer executable components and/or computer executableinstructions. The system 100 may also include at least one processor112, communicatively coupled to the at least one memory 110. The atleast one processor 112 may facilitate execution of the computerexecutable components and/or the computer executable instructions storedin the at least one memory 110. The term “coupled” or variants thereofmay include various communications including, but not limited to, directcommunications, indirect communications, wired communications, and/orwireless communications.

It is noted that although the one or more computer executable componentsand/or computer executable instructions may be illustrated and describedherein as components and/or instructions separate from the at least onememory 110 (e.g., operatively connected to the at least one memory 110),the various aspects are not limited to this implementation. Instead, inaccordance with various implementations, the one or more computerexecutable components and/or the one or more computer executableinstructions may be stored in (or integrated within) the at least onememory 110. Further, while various components and/or instructions havebeen illustrated as separate components and/or as separate instructions,in some implementations, multiple components and/or multipleinstructions may be implemented as a single component or as a singleinstruction. Further, a single component and/or a single instruction maybe implemented as multiple components and/or as multiple instructionswithout departing from the example embodiments.

FIG. 2 illustrates an example, non-limiting generic schematicrepresentation of a capacity architecture 200, according to an aspect.With the increase in the amount of data that is available and collectedfrom various sources, the data should be organized in a manner thatallows the data to be easily found and utilized on an as needed basis.

The architecture 200 illustrated is generic in order to describe variousaspects. The overall architecture 200 relates to a phenomenon 202, whichis the subject matter under investigation (e.g., financial management,cognitive healthcare, and so on). The subject matter or phenomenon 202may include almost anything and some examples include financialmanagement, cognitive healthcare, insurance, and so on.

A concept related to the various aspects discussed herein is complexitytheory. In its simplest terms, complexity theory states that somethingis complex because something emerges (e.g., the term emerging) that isunexpected from a set of actions and reactions by certain agents. Forexample, there may be a group of agents that respond to something in theenvironment and that react without any central controller (e.g., eachagent acts independently). The agents are illustrated as external agents204 and internal agents 206. The external agents 204 are agents that arenot under the direction or control of a central source (e.g., anemployer). Examples of external agents include customers, individuals,and so on. The internal agents 206 are agents that are directed orcontrolled by a central source (e.g., an employer). As utilized hereinan agent, a user, a client, a customer, an entity, or the like, mayrefer to a human, an actor, a computer, the Internet, a system (oranother system), a commercial enterprise, a computer, a machine,machinery, and so forth, hereinafter referred to as an agent, a user, acustomer, and/or an entity, depending on the context.

As one example, the internal agents 206 of a financial entity may belines of business and individuals within the line of business actwithout anyone controlling them or without regard to the effect on otherlines of business. For example, someone from outside the bank causesindividuals within the bank to determine they must act immediately. Thisis when irrational behavior may occur. The framework of the variousaspects discussed herein address those emerging behaviors. Thus, thevarious aspects not only predict future actions, but also prescribe howpeople should act. Accordingly, the various aspects provide at leastthree levels of analytics, namely, descriptive, predictive, andprescriptive analytics.

With continuing reference to FIG. 2 , the way complexity theory isexplained is that there are a set of agents (e.g., external agents 204and internal agents 206) that have a mind of their own—each agent maysend and respond. The external agents 204 have self-organizing behavior208 and the internal agents 206 have self-organizing behavior 210.According to some implementations, the external agents 204 and/or theinternal agents 206 may take stimulus from the cognitive systems 212they interface with (in this example, the bank).

The self-organizing behavior 208 and/or the self-organizing behavior 210represent two-way self-organizing behavior. The cognitive systems 212(e.g., the bank) adapts to what the external agents 204 and/or theinternal agents 206 are doing. Further, the agents 204, 206 adapt towhat the cognitive systems 212 are doing. For example, the bank takesinto consideration the internal systems and/or procedures implementedwithin the bank. Further, the bank takes into consideration what ishappening in the outside world (e.g., outside influences not controlledby the bank).

As illustrated, within the cognitive systems 212 are two genericportions, a taxonomy 214 and enablers 216. The taxonomy 214 may be anytype, provided it allows the knowledge to be organized. In one example,the taxonomy 214 may be a business architecture that relates to rulesand/or policies established by the business. The enablers 216 are thesmart computing, data visualization, and other concepts andtechnologies.

In one implementation, the phenomenon 202 may be financial management.However, the disclosed aspects are not limited to this implementation.Instead, the phenomenon may be other aspects of banking, insurance, andso on. It is noted that the term “financial management” may beapplicable to both a consumer as well as an organization. Financialmanagement from an individual perspective may be financial lifemanagement (e.g., how to create wealth, what kind of financial decisionsdo I make that will help me optimize what I have today, and so on). Theconcept of financial management may be expanded to the external agents204 (e.g., consumers) and internal agents 206 (e.g., employees,shareholders, business partners, and so forth). For example, theinternal agents 206 may also ask questions related to what kind offinancial decisions should be made today that will optimize resourcesfor the future.

FIG. 3 illustrates an example schematic representation of a capacityarchitecture 300, according to an aspect. The capacity architecture 300of FIG. 3 is a specific implementation of the capacity architecture 200of FIG. 2 .

As illustrated, the capacity architecture 300 may be divided into threemain sections. A first section is a Cognitive and Heuristic BasedEmerging Financial Management (CHEF) section 302. The CHEF section 302may be configured to capture the benefits of various technologiesincluding, for example, SMAC (social, mobile, analytics, and cloud), bigdata, and so on. A second section is a Personal Asset Tracking (PASTRY)section 304. A third section is a Capability Architecture Knowledge Basefor an Enterprise (CAKE) section 306. Further details related to thesesections will be provided below.

In the specific implementation of the capacity architecture 300, theexternal agents are customers 308 that are requesting decision support310. Further, the internal agents are team members 312 that arerequesting decision support 314. The decision support 310, 314 (e.g.,self-organizing behavior) in this example may be similar — the customers308 and team members 312 need help in making certain decisions relatedto financial management 316 (e.g., the phenomenon 202 of FIG. 2 ).

The cognitive systems 318 include a business capacity architecture 320and cloud computing 322. The cloud computing 322 may include, forexample, big data, interne of things (IoT), SMAC, data visualization,visual analytics, application programing interface (API) economy, andother technologies. The business capacity architecture 320 includesvarious components such as strategy, organization, capability, process,application, project, partner, product, customer, and so on. Furtherinformation related to the interrelationships within the businesscapacity architecture 320 for this example is illustrated in FIGS. 4Aand 4B. FIG. 4A illustrates a left hand side of an example, non-limitingCAKE data map 400 and FIG. 4B illustrates a right hand side of the CAKEdata map 400. The box labeled capacity 402 is a central point on bothview of the CAKE data map 400.

Components of the business capacity architecture 320, include, forexample, capability 402, application 404, strategy 406, organization408, process, 410, product, 412, project 414, partner 416, and customer418. The other blocks within FIGS. 4A and 4B represent the system ofrecord. For example, the systems of record for customer 418, includefraud 420, internet of things (IoT) 422, social media 424, and APS 426.The system of record for the other business capacity architecturecomponents are not labeled for purposes of simplicity. Each source ofrecord may be further divided into specific sources, as illustrated.

The CAKE data map 400 represents the concept that, if you have big datain a particular box, where is the big data located, what systems are inplace, and so on. For example, if looking at customer profileinformation, fraud analytic information may be analyzed to determine ifthe customer is logged in using a smart phone, an online session, and soon. Further information is discussed in Appendix A and Appendix B.Appendix A is a document describing various embodiments and featuresassociated with particular aspects of the various aspects—this appendixis to be considered part of the specification. Appendix B is a documentdescribing various aspects and features associated with particularembodiments—this appendix is to be considered part of the specification.

FIG. 5 illustrates an example, non-limiting system 500 that providescognitive and heuristics-based emergent financial management, accordingto an aspect. A customer interacts with the system 500 (or the system500 receives information from a team member) through an interfacecomponent 502. According to some implementations, each user may interactwith the system 500 through their user device (e.g., mobile phone,personal computer, and so on). This information may be communicated fromthe respective user device to the interface component 502 or to anothersystem component.

The customer and the team member (e.g., external agents and internalagents) interact with the system, information flows back and forth andthe interrelationship between business capability architecture supportsthe interaction to drive decisions on how things are done.

Also included in the system 500 may be a data manager 504 that may beconfigured to manage the received data. For example, the data manager504 may flagged or otherwise mark to data to indicate whether the datais historical data 506, current data 508, or estimated data 510. Thehistorical data 506 is any data that has been gathered from a user or aset of users over time. The current data 506 relates to a currentactivity or other data of the user (e.g., location, activity, and soon). The estimated data 510 is a projection of what is likely to occur(e.g., if you wait forty days you will save money).

The following is a use-case scenario, an individual (referred to as Tom)is driving through his neighborhood and sees a house for sale. Byinteracting with a device (e.g., mobile phone, smart watch, personalcomputer, and so on), Tom interacts with the system 500 (e.g., throughthe interface component 502) and asks whether he should buy or rent ahouse, or whether a particular house for sale would be a goodinvestment. In this example, Tom is interacting with the PASTRY 304section of FIG. 3 .

The location of Tom may be known, such as through a GPS location orother means of determining the location. According to someimplementations, the location is known based on information receivedfrom a device associated with Tom (e.g., a user device), which may beconveyed to the interface component 502 or another system component.

Further, the data manager 504 already has information about Tom, such ashow much money he has saved, how much he can use for a down payment,spending habits, and so on. Further, the data manager 504 hasinformation related to what is happening in the market, what Tom's peersare doing, and so on. Based on the known information, the data manager504 (e.g. the PASTRY section 304 section of FIG. 3 ) looks ahead and mayprovide advice in real-time (e.g., within seconds and while Tom isdriving through the neighborhood).

With reference again to FIGS. 4A and 4B, the cake data mine map 400 isan example, non-limiting DNA of the bank. The central components arecapability 402, application 404, strategy 406, organization 408,process, 410, product, 412, project 414, partner 416, and customer 418.As illustrated, possible systems of application 404 include “cloud,”“remedy,” and “Intranet.” With reference to Intranet, there are emails,people have further discussions, and how all the data is assimilated ororganized in order for the various aspects discussed herein to performthe insightful decision making scenario for the customer (or otheragent).

With reference to the customer 308 branch, there is fraud 420, IoT 422,social media 424, and APS 426. Associated with APS 426 are differentchannel data, which may include online channels, mobile channels, ATMs,stores, phones, and so on (e.g., augmented reality). All of thisinformation is collected (e.g., streaming data, operational data, nearreal-time data) and processed. This is one of the values of the CAKEdata mine map 402. For example, it is easily demonstrable and determinedthat customer is connected to product, which is connected toorganization, which is connected to capability.

Traditional systems organize data based on processes. For example, theremay be a business process model where SORs are mapped to a set ofprocesses (e.g., collections, such as collecting money or openaccounts). The concept of SORs explains the relationship between astimulus (S) the customer receives, what emotions the customer feels intheir organism (O), and the customer's responses or attitudes (R). Theproblem with the traditional systems is that those systems are notstaple architectures because at any time the process may change.Therefore, a person has to go back to the taxonomy and the mapping inthe taxonomy and manually move the lines back and forth to connect thenew components and remove the old components as appropriate.

Accordingly, an advantage of the various aspects discussed herein isthat the capacity mapping is a highly stable abstraction of the variousfunctions performed. For example, a capability is “sales” and under“sales” is “underwriting,” which has a capability that banks (and otherentities) perform on a daily basis. Therefore, this does not change, butthe process may change. Some are semi-automated, others fully automated,using robots, for example. However, regardless of whether organizationsare a mixture of robot and human, the process of underwriting, the toolsused (e.g., pen and paper, financial software, and so forth) does notmatter because the underlying capability will be what the bank does on adaily basis. Therefore, the lines (in FIGS. 4A and 4B) that connect arecapabilities, which seldom change over a period of time.

According to an additional or alternative implementation, another waythat data may be organized is from a technology focus. Take for example,an application management lifecycle, which may be in an InformationTechnology (IT) framework (e.g., an IT infrastructure library). This islooked at in terms of systems development, systems maintenance, helpdesk, and so on. Thus, all the SORs in the bank and all the systems willfall under any of those technology processes. It is noted that althoughvarious use cases of technology have been discussed herein, these usecases do not encompass all possible use cases and/or technologies.

With reference again to FIG. 3 , the CAKE portion 306 may be a decisionsupport tool for executives in the bank in terms of the same questions(or different questions) posed by the customers (e.g., should I buy orrent?). In another example, the CAKE portion 306 may be a decisionsupport tool that advises a line of business executive whether theyshould outsource to an external team or keep the functions internal tothe bank.

In another example, the question posed may be, “Do I have the capabilityto underwrite or should I outsource it?”. For the executive to make thatdecision herself, she needs to have a way of aggregating and makingsense out of the data that exists in the bank, and knowing that thedifferent groups may operate in silos within the bank. With traditionalsystems, this may only be available to the decision makers in the bank.They not only have a point of view as to how the organization is set uptoday, but what are the key strategic initiatives of the organizationand the other lines of business with which a target line of businessshould be aligned. Other question may be: “How am I compared to otherlines of business in this field called outsourcing?” “What will be theimpact of different applications for the bank about moving people towork outside the bank?” “What is being done on the customer experienceand the capability of a toll-free telephone line if any questions ariseif someone outside the bank is answering the phone?” “How aboutregulations and export of information outside USA?” This questions maybe answered today, however, it may take months for a decision and alarge number of resources have to be expended.

According to some implementations, the CAKE portion 306 is output as adashboard. The dashboard may sit on top of (e.g., overlaid on) a sharedplatform. Therefore, the operational data, strategic data, streamingdata, context data, and so on, may be seen at substantially the sametime as actionable insights.

An example, non-limiting organization dashboard is illustrated in Page 7of Appendix A. An illustration of an example, non-limiting projectdashboard is illustrated in Page 8 of Appendix A. The right side(although it could be located at another portion) provides the abilityto filter the information using drop down menus (or other means ofselection). Although certain filters are illustrated, the data may befiltered utilizing other data.

An example, non-limiting OPS sourcing lever dashboard is illustrated inPage 9 of Appendix A. This dashboard represents an aggregation oforganization and projects. As illustrated on the right-hand side (orother portion of the screen) filtering mechanisms are provided.

With reference again to FIG. 5 , according to some implementations, theinterface component 502 (as well as other interface components discussedherein) may provide a graphical user interface (GUI), a command lineinterface, a speech interface, Natural Language text interface, and thelike. For example, a Graphical User Interface (GUI) may be rendered thatprovides a user with a region or means to load, import, select, read,and so forth, various requests and may include a region to present theresults of the various requests. These regions may include known textand/or graphic regions that include dialogue boxes, static controls,drop-down-menus, list boxes, pop-up menus, as edit controls, comboboxes, radio buttons, check boxes, push buttons, graphic boxes, and soon. In addition, utilities to facilitate the information conveyance,such as vertical and/or horizontal scroll bars for navigation andtoolbar buttons to determine whether a region will be viewable, may beemployed. Thus, it might be inferred that the user did want the actionperformed.

The user may also interact with the regions to select and provideinformation through various devices such as a mouse, a roller ball, akeypad, a keyboard, a pen, gestures captured with a camera, a touchscreen, and/or voice activation, for example. According to an aspect, amechanism, such as a push button or the enter key on the keyboard, maybe employed subsequent to entering the information in order to initiateinformation conveyance. However, it is to be appreciated that thedisclosed aspects are not so limited. For example, merely highlighting acheck box may initiate information conveyance. In another example, acommand line interface may be employed. For example, the command lineinterface may prompt the user for information by providing a textmessage, producing an audio tone, or the like. The user may then providesuitable information, such as alphanumeric input corresponding to anoption provided in the interface prompt or an answer to a question posedin the prompt. It is to be appreciated that the command line interfacemay be employed in connection with a GUI and/or Application ProgramInterface (API). In addition, the command line interface may be employedin connection with hardware (e.g., video cards) and/or displays (e.g.,black and white, and Video Graphics Array (EGA)) with limited graphicsupport, and/or low bandwidth communication channels.

FIG. 6 illustrates an example, non-limiting system 600 that employsautomated learning to facilitate one or more of the disclosed aspects.For example, a machine learning and reasoning component 602 may beutilized to automate one or more of the disclosed aspects. The machinelearning and reasoning component 602 may employ automated learning andreasoning procedures (e.g., the use of explicitly and/or implicitlytrained statistical classifiers) in connection with performing inferenceand/or probabilistic determinations and/or statistical-baseddeterminations in accordance with one or more aspects described herein.

For example, the machine learning and reasoning component 602 may employprinciples of probabilistic and decision theoretic inference.Additionally or alternatively, the machine learning and reasoningcomponent 602 may rely on predictive models constructed using machinelearning and/or automated learning procedures. Logic-centric inferencemay also be employed separately or in conjunction with probabilisticmethods.

The machine learning and reasoning component may infer how data shouldbe organized in comparison to other data, which data to utilize torespond to a question, whether a particular set of data should be rankedhigher than another set of data to provide a recommended course ofaction, whether data should be cross-referenced between differentorganizational categories, and so on. Based on this knowledge, themachine learning and reasoning component 602 may make an inference basedon historical data, current data, estimated data and the results of theestimated data (e.g., based on a feedback loop), and so on.

As used herein, the term “inference” refers generally to the process ofreasoning about or inferring states of the system, a component, amodule, the environment, and/or customers (or devices associated withthe customers) from a set of observations as captured through events,reports, data, and/or through other forms of communication. Inferencemay be employed to identify a specific context or action, or maygenerate a probability distribution over states, for example. Theinference may be probabilistic. For example, computation of aprobability distribution over states of interest based on aconsideration of data and/or events. The inference may also refer totechniques employed for composing higher-level events from a set ofevents and/or data. Such inference may result in the construction of newevents and/or actions from a set of observed events and/or stored eventdata, whether or not the events are correlated in close temporalproximity, and whether the events and/or data come from one or severalevents and/or data sources. Various classification schemes and/orsystems (e.g., support vector machines, neural networks, logic- centricproduction systems, Bayesian belief networks, fuzzy logic, data fusionengines, and so on) may be employed in connection with performingautomatic and/or inferred action in connection with the disclosedaspects.

The various aspects (e.g., in connection with facilitating acollaborative framework or dashboard for addressing issues in real-time)may employ various artificial intelligence- based schemes for carryingout various aspects thereof. For example, a process for determining apriority of a particular set of data, what data should be gathered toanalyze certain issues, how to organize one or more sets of data, how tocross-reference sets of data, and so on may be enabled through anautomatic classifier system and process.

A classifier is a function that maps an input attribute vector, x=(x1,x2, x3, x4, xn), to a confidence that the input belongs to a class. Inother words, f(x)=confidence(class). Such classification may employ aprobabilistic and/or statistical-based analysis (e.g., factoring intothe analysis utilities and costs) to prognose or infer an action thatshould be employed to determine how data should be organized, which datashould be analyzed to respond to an issue, and so on. In the case ofcognitive and heuristics-based emergent financial management, forexample, attributes may be keywords or phrases in a data set and theclasses may be identification of an identified issue.

A support vector machine (SVM) is an example of a classifier that may beemployed. The SVM operates by finding a hypersurface in the space ofpossible inputs, which hypersurface attempts to split the triggeringcriteria from the non-triggering events. Intuitively, this makes theclassification correct for testing data that may be similar, but notnecessarily identical to training data. Other directed and undirectedmodel classification approaches (e.g., naive Bayes, Bayesian networks,decision trees, neural networks, fuzzy logic models, and probabilisticclassification models) providing different patterns of independence maybe employed. Classification as used herein, may be inclusive ofstatistical regression that is utilized to develop models of priority.

One or more aspects may employ classifiers that are explicitly trained(e.g., through a generic training data) as well as classifiers that areimplicitly trained (e.g., by observing user behavior, by receivingextrinsic information, and so on). For example, SVM's may be configuredthrough a learning or training phase within a classifier constructor andfeature selection module. Thus, a classifier(s) may be used toautomatically learn and perform a number of functions, including but notlimited to determining according to a predetermined criteria whichresponse should be given based on historical data related to the same ora similar issue, which data should be output, whether to include one ormore sets of data for analysis, whether the predication was correct ornot, and so forth. The criteria may include, but is not limited to,similar information, historical information, current information, issue(e.g., question) attributes, and so forth.

Additionally or alternatively, an implementation scheme (e.g., a rule, apolicy, and so on) may be applied to control and/or regulate which issuesubmissions are considered to be routine and most likely has more datathat may be analyzed. In some implementations, based upon a predefinedcriterion, the rules-based implementation may automatically and/ordynamically interpret attributes associated with each issue. In responsethereto, the rule- based implementation may automatically interpret andcarry out functions associated with the issues by employing a predefinedand/or programmed rule(s) based upon any desired criteria.

Methods that may be implemented in accordance with the disclosed subjectmatter, will be better appreciated with reference to the following flowcharts. While, for purposes of simplicity of explanation, the methodsare shown and described as a series of blocks, it is to be understoodand appreciated that the disclosed aspects are not limited by the numberor order of blocks, as some blocks may occur in different orders and/orat substantially the same time with other blocks from what is depictedand described herein. Moreover, not all illustrated blocks may berequired to implement the disclosed methods. It is to be appreciatedthat the functionality associated with the blocks may be implemented bysoftware, hardware, a combination thereof, or any other suitable means(e.g. device, system, process, component, and so forth). Additionally,it should be further appreciated that the disclosed methods are capableof being stored on an article of manufacture to facilitate transportingand transferring such methods to various devices. Those skilled in theart will understand and appreciate that the methods might alternativelybe represented as a series of interrelated states or events, such as ina state diagram.

FIG. 7 illustrates an example, non-limiting method 700 for cognitive andheuristics-based emergent financial management, according to an aspect.The method 700 in FIG. 7 may be implemented using, for example, any ofthe systems, such as the system 500 (of FIG. 5 ), described herein.

The method 700 starts at 702, when data related to an individual (ormore than one individual is obtained). Further, data related to anorganization and/or processes may be obtained. The data may be obtainedfrom internal sources, external sources, or combinations thereof (bothinternal and external sources). The data may also be related to abusiness organization or other individuals (e.g., peers). Further, thedata may be in various formats including, for example, a structuredformat, a semi-structured format, an unstructured format, orcombinations thereof

At 704, data sets are created from the data. The data sets may becreated based on determined classifications of the data. For example,the data sets may be categorized based on capabilities. Relationshipsbetween data sets are created, at 706. For example, the relationshipsmay be based on determined classifications of the data.

A conclusion based on the relationships is determined, at 708. Theconclusion may be provided in response to a question posed (e.g., issuepresented). According to some implementations, the conclusion may bebased on a hypothesis that has undergone a test process.

One or more implementations include a computer-readable medium includingmicroprocessor or processor-executable instructions configured toimplement one or more embodiments presented herein. As discussed hereinthe various aspects enable cognitive and heuristics-based emergentfinancial management. An embodiment of a computer-readable medium or acomputer-readable device devised in these ways is illustrated in FIG. 8, wherein an implementation 800 includes a computer-readable medium 802,such as a CD-R, DVD-R, flash drive, a platter of a hard disk drive, andso forth, on which is encoded computer-readable data 804. Thecomputer-readable data 804, such as binary data including a plurality ofzero's and one's as illustrated, in turn includes a set of computerinstructions 806 configured to operate according to one or more of theprinciples set forth herein.

In the illustrated embodiment 800, the set of computer instructions 806(e.g., processor-executable computer instructions) may be configured toperform a method 808, such as the method 700 of FIG. 7 , for example. Inanother embodiment, the set of computer instructions 806 may beconfigured to implement a system, such as the system 100 of FIG. 1and/or the system 600 of FIG. 6 , for example. Many suchcomputer-readable media may be devised by those of ordinary skill in theart that are configured to operate in accordance with the techniquespresented herein.

As used in this application, the terms “component,” “module,” “system,”“interface,” “manager,” and the like are generally intended to refer toa computer-related entity, either hardware, a combination of hardwareand software, software, or software in execution. For example, acomponent may be, but is not limited to being, a process running on aprocessor, a processor, an object, an executable, a thread of execution,a program, or a computer. By way of illustration, both an applicationrunning on a controller and the controller may be a component. One ormore components residing within a process or thread of execution and acomponent may be localized on one computer or distributed between two ormore computers.

Further, the claimed subject matter may be implemented as a method,apparatus, or article of manufacture using standard programming orengineering techniques to produce software, firmware, hardware, or anycombination thereof to control a computer to implement the disclosedsubject matter. The term “article of manufacture” as used herein isintended to encompass a computer program accessible from anycomputer-readable device, carrier, or media. Of course, manymodifications may be made to this configuration without departing fromthe scope or spirit of the claimed subject matter.

FIG. 8 and the following discussion provide a description of a suitablecomputing environment to implement embodiments of one or more of theaspects set forth herein. The operating environment of FIG. 8 is merelyone example of a suitable operating environment and is not intended tosuggest any limitation as to the scope of use or functionality of theoperating environment. Example computing devices include, but are notlimited to, personal computers, server computers, hand-held or laptopdevices, mobile devices, such as mobile phones, Personal DigitalAssistants (PDAs), media players, and the like, multiprocessor systems,consumer electronics, mini computers, mainframe computers, distributedcomputing environments that include any of the above systems or devices,etc.

Generally, embodiments are described in the general context of “computerreadable instructions” being executed by one or more computing devices.Computer readable instructions may be distributed via computer readablemedia as will be discussed below. Computer readable instructions may beimplemented as program modules, such as functions, objects, ApplicationProgramming Interfaces (APIs), data structures, and the like, thatperform one or more tasks or implement one or more abstract data types.Typically, the functionality of the computer readable instructions arecombined or distributed as desired in various environments.

FIG. 9 illustrates a system 900 that may include a computing device 902configured to implement one or more embodiments provided herein. In oneconfiguration, the computing device 902 may include at least oneprocessing unit 904 and at least one memory 906. Depending on the exactconfiguration and type of computing device, the at least one memory 906may be volatile, such as RAM, non-volatile, such as ROM, flash memory,etc., or a combination thereof. This configuration is illustrated inFIG. 9 by dashed line 908.

In other embodiments, the computing device 902 may include additionalfeatures or functionality. For example, the computing device 902 mayinclude additional storage such as removable storage or non-removablestorage, including, but not limited to, magnetic storage, opticalstorage, etc. Such additional storage is illustrated in FIG. 9 bystorage 910. In one or more embodiments, computer readable instructionsto implement one or more embodiments provided herein are in the storage910. The storage 910 may store other computer readable instructions toimplement an operating system, an application program, etc. Computerreadable instructions may be loaded in the at least one memory 906 forexecution by the at least one processing unit 904, for example.

Computing devices may include a variety of media, which may includecomputer-readable storage media or communications media, which two termsare used herein differently from one another as indicated below.

Computer-readable storage media may be any available storage media,which may be accessed by the computer and includes both volatile andnonvolatile media, removable and non-removable media. By way of example,and not limitation, computer-readable storage media may be implementedin connection with any method or technology for storage of informationsuch as computer-readable instructions, program modules, structureddata, or unstructured data. Computer-readable storage media may include,but are not limited to, RAM, ROM, EEPROM, flash memory or other memorytechnology, CD-ROM, digital versatile disk (DVD) or other optical diskstorage, magnetic cassettes, magnetic tape, magnetic disk storage orother magnetic storage devices, or other tangible and/or non-transitorymedia which may be used to store desired information. Computer-readablestorage media may be accessed by one or more local or remote computingdevices (e.g., via access requests, queries or other data retrievalprotocols) for a variety of operations with respect to the informationstored by the medium.

Communications media typically embody computer-readable instructions,data structures, program modules, or other structured or unstructureddata in a data signal such as a modulated data signal (e.g., a carrierwave or other transport mechanism) and includes any information deliveryor transport media. The term “modulated data signal” (or signals) refersto a signal that has one or more of its characteristics set or changedin such a manner as to encode information in one or more signals. By wayof example, and not limitation, communication media include wired media,such as a wired network or direct-wired connection, and wireless mediasuch as acoustic, RF, infrared and other wireless media.

The computing device 902 may include input device(s) 912 such askeyboard, mouse, pen, voice input device, touch input device, infraredcameras, video input devices, or any other input device. Outputdevice(s) 914 such as one or more displays, speakers, printers, or anyother output device may be included with the computing device 902. Theinput device(s) 912 and the output device(s) 914 may be connected to thecomputing device 902 via a wired connection, wireless connection, or anycombination thereof. In one or more embodiments, an input device or anoutput device from another computing device may be used as the inputdevice(s) 912 and/or the output device(s) 914 for the computing device902. Further, the computing device 902 may include communicationconnection(s) 916 to facilitate communications with one or more otherdevices, illustrated as a computing device 918 coupled over a network920.

One or more applications 922 and/or program data 924 may be accessibleby the computing device 902. According to some implementations, theapplication(s) 922 and/or program data 924 are included, at least inpart, in the computing device 902. The application(s) 922 may include acognitive and heuristics-based emergent financial management algorithm926 that is arranged to perform the functions as described hereinincluding those described with respect to the system 300 of FIG. 3 . Theprogram data 924 may include cognitive and heuristics-based emergentfinancial management commands and cognitive and heuristics-basedemergent financial management information 928 that may be useful foroperation with the various aspects as described herein.

Although the subject matter has been described in language specific tostructural features or methodological acts, it is to be understood thatthe subject matter of the appended claims is not necessarily limited tothe specific features or acts described above. Rather, the specificfeatures and acts described above are disclosed as example embodiments.

Various operations of embodiments are provided herein. The order inwhich one or more or all of the operations are described should not beconstrued as to imply that these operations are necessarily orderdependent. Alternative ordering will be appreciated based on thisdescription. Further, not all operations may necessarily be present ineach embodiment provided herein.

A device may also be called, and may contain some or all of thefunctionality of a system, subscriber unit, subscriber station, mobilestation, mobile, mobile device, wireless terminal, device, remotestation, remote terminal, access terminal, user terminal, terminal,wireless communication device, wireless communication apparatus, useragent, user device, or user equipment (UE). A mobile device may be acellular telephone, a cordless telephone, a Session Initiation Protocol(SIP) phone, a smart phone, a feature phone, a wireless local loop (WLL)station, a personal digital assistant (PDA), a laptop, a handheldcommunication device, a handheld computing device, a netbook, a tablet,a satellite radio, a data card, a wireless modem card, and/or anotherprocessing device for communicating over a wireless system. Further,although discussed with respect to wireless devices, the disclosedaspects may also be implemented with wired devices, or with both wiredand wireless devices.

As used in this application, “or” is intended to mean an inclusive “or”rather than an exclusive “or.” Further, an inclusive “or” may includeany combination thereof (e.g., A, B, or any combination thereof). Inaddition, “a” and “an” as used in this application are generallyconstrued to mean “one or more” unless specified otherwise or clear fromcontext to be directed to a singular form. Additionally, at least one ofA and B and/or the like generally means A or B or both A and B. Further,to the extent that “includes”, “having”, “has,” “with,” or variantsthereof are used in either the detailed description or the claims, suchterms are intended to be inclusive in a manner similar to the term“comprising”.

Further, unless specified otherwise, “first,” “second,” or the like arenot intended to imply a temporal aspect, a spatial aspect, an ordering,etc. Rather, such terms are merely used as identifiers, names, etc. forfeatures, elements, items, etc. For example, a first channel and asecond channel generally correspond to channel A and channel B or twodifferent or two identical channels or the same channel. Additionally,“comprising,” “comprises,” “including,” “includes,” or the likegenerally means comprising or including.

Although the disclosure has been shown and described with respect to oneor more implementations, equivalent alterations and modifications willoccur based on a reading and understanding of this specification and theannexed drawings. The disclosure includes all such modifications andalterations and is limited only by the scope of the following claims.

What is claimed is:
 1. A system comprising: a processor configured to:process using natural language text processing user input comprising oneor more questions posed by a user; receive user data related to the oneor more questions posed by the user; receive financial data related toone or more of an individual, an organization, a process, orcombinations thereof, and including at least one of interest rate ormacroeconomic financial information; create data sets based ondetermined classifications of the user data and the financial data; andestablish relationships between the data sets; wherein creating the datasets and establishing the relationships between the data sets comprises:determining how the user data and the financial data should be organizedin comparison to other financial data by comparing past financial data,current financial data, and results of estimated financial data, whereinthe results of the estimated financial data are based on a feedbackloop; employing, by a machine learning and reasoning component,classifiers comprising explicitly trained classifiers, implicitlytrained classifiers or a combination of explicitly and implicitlytrained classifiers; and determining a conclusion based on therelationships, wherein the conclusion comprises performing a testprocess on a hypothesis.
 2. The system of claim 1, wherein the processoris further configured to facilitate developing a personal managementstrategy for a customer based on the conclusion.
 3. The system of claim1, wherein the user data and the financial data are in one or more of astructured format, a semi-structured format, an unstructured format, orcombinations thereof.
 4. The system of claim 1, wherein the user dataand the financial data are acquired from an internal source or anexternal source.
 5. The system of claim 1, wherein the user data and thefinancial data are acquired from an internal source and an externalsource.
 6. The system of claim 5, wherein the external source comprisesa location determined from a Global Positioning System (GPS).
 7. Thesystem of claim 1, wherein creating data sets is further based on acapacity architecture that organizes the user data and the financialdata based at least in part on capability of functions related to theuser data and the financial data.
 8. The system of claim 7, wherein thecapacity architecture is comprised of a cognitive and heuristic basedemerging financial management section, a personal asset tracking sectionand a capability architecture knowledge base for an enterprise section.9. The system of claim 8, wherein the capability architecture knowledgebase for an enterprise section comprises a data organization componentthat cross-references processes, technology and astimulus-organism-response mapping.
 10. The system of claim 1, whereinthe processor is further configured to output to a user interface aGraphical User Interface (GUI).
 11. A computer implemented methodcomprising: processing using natural language text processing user inputcomprising one or more questions posed by a user; receiving user datarelated to the one or more questions posed by the user; receivingfinancial data related to one or more of an individual, an organization,a process, or combinations thereof, and including at least one ofinterest rate or macroeconomic financial information; creating data setsbased on determined classifications of the user data and the financialdata; and establishing relationships between the data sets; whereincreating the data sets and establishing the relationships between thedata sets comprises: determining how the user data and the financialdata should be organized in comparison to other financial data bycomparing past financial data, current financial data, and results ofestimated financial data, wherein the results of the estimated financialdata are based on a feedback loop; employing, by a machine learning andreasoning component, classifiers comprising explicitly trainedclassifiers, implicitly trained classifiers or a combination ofexplicitly and implicitly trained classifiers; and determining aconclusion based on the relationships, wherein the conclusion comprisesperforming a test process on a hypothesis.
 12. The computer implementedmethod of claim 11, further comprising developing a personal managementstrategy for a customer based on the conclusion.
 13. The computerimplemented method of claim 11, wherein the user data and the financialdata are in one or more of a structured format, a semi-structuredformat, an unstructured format, or combinations thereof.
 14. Thecomputer implemented method of claim 11, wherein the user data and thefinancial data are acquired from an internal source or an externalsource.
 15. The computer implemented method of claim 11, wherein theuser data and the financial data are acquired from an internal sourceand an external source.
 16. The computer implemented method of claim 15,wherein the external source comprises a location determined from aGlobal Positioning System (GPS).
 17. The computer implemented method ofclaim 11, wherein creating data sets is further based on a capacityarchitecture that organizes the user data and the financial data basedat least in part on capability of functions related to the user data andthe financial data.
 18. The computer implemented method of claim 17,wherein the capacity architecture is comprised of a cognitive andheuristic based emerging financial management section, a personal assettracking section and a capability architecture knowledge base for anenterprise section.
 19. The computer implemented method of claim 18,wherein the capability architecture knowledge base for an enterprisesection comprises a data organization component that cross-referencesprocesses, technology and a stimulus-organism-response mapping.
 20. Anon-transitory computer readable medium comprising program code thatwhen executed by one or more processors is configured to cause the oneor more processors to: process using natural language text processinguser input comprising one or more questions posed by a user; receiveuser data related to the one or more questions posed by the user;receive financial data related to one or more of an individual, anorganization, a process, or combinations thereof, and including at leastone of interest rate or macroeconomic financial information; create datasets based on determined classifications of the user data and thefinancial data; and establish relationships between the data sets;wherein creating the data sets and establishing the relationshipsbetween the data sets comprises: determining how the user data and thefinancial data should be organized in comparison to other financial databy comparing past financial data, current financial data, and results ofestimated financial data, wherein the results of the estimated financialdata are based on a feedback loop; employing, by a machine learning andreasoning component, classifiers comprising explicitly trainedclassifiers, implicitly trained classifiers or a combination ofexplicitly and implicitly trained classifiers; and determining aconclusion based on the relationships, wherein the conclusion comprisesperforming a test process on a hypothesis.